北京交通大学学报2018，Vol.42Issue(2)：38-45,8.DOI:10.11860/j.issn.1673-0291.2018.02.006
采用传播图论建模方法的Massive MIMO室内场景传播特性
Massive MIMO propagation characteristics in indoor scenario based on propagation graph modeling
摘要
Abstract
The massive multiple input and multiple output (Massive MIMO) system,equipped large-scale antenna array in the base station,can greatly improve the data transmission speed and the system capacity and achieve the maximum utilization of the time and space resources.It's of great significance for the application of large-scale antenna and the design of the system to research the wireless communication channel of the Massive MIMO.In this paper,a channel modeling approach based on propagation graph theory is adopted to study the performance of Massive MIMO in the actual propagation channel,which is efficient and able to avoid the heavy workload of practical measurement.For a certain scenario,the channel modeling and simulation is conducted at both 6 GHz and 1.472 5 GHz.The channel impulse response derived from the propagation graph modeling can be further used for parameter extraction.The propagation characteristics of Massive MIMO channel is analyzed from the angle domain,time domain and the singular value decomposition separately.关键词
无线通信/大规模多天线/传播图论/信道建模/信道传播特性/奇异值扩展Key words
wireless communication/massive multiple input and multiple output/propagation graph theory/channel modeling/channel propagation characteristics/singular value decomposition分类
信息技术与安全科学引用本文复制引用
刘留,刘妍,雷勇,吴钰浩..采用传播图论建模方法的Massive MIMO室内场景传播特性[J].北京交通大学学报,2018,42(2):38-45,8.基金项目
中央高校基本科研业务费专项资金(2017JBM306) （2017JBM306）
北京市科技新星计划项目(Z161100004916068) （Z161100004916068）
国家自然科学基金面上项目(61471027) （61471027）
东南大学移动通信国家重点实验室开放研究基金(2017D01) （2017D01）
北京市自然科学基金(L172030)Fundamental Research Funds for the Central Universities(2017JBM306) （L172030）
Beijing Nova Program (Z161100004916068) （Z161100004916068）
National Natural Science Foundation of China (61471027) （61471027）
Research Fund of National Mobile Communications Research Laboratory,Southeast University (2017D01) （2017D01）
Beijing Natural Science Foundation(L172030) （L172030）
